Intelligent routing control

ABSTRACT

A system and method classify insurance documents. The system and method receive a request to access documents from an assessment queue stored in a memory of a device where each of the documents contain insurance data and have an associated destination and associated metadata. The system and method convert the set of documents encoded in a first file format to a second file format to remove the metadata. The system and method partition each document of the set of documents into separate stand-alone documents and converts each of the partitioned documents into separate recognition vectors. The system and method classifies the partitioned documents through an additive learning algorithm in which routing data is embedded in second metadata and merge the classified partitioned documents. The merged documents are routed to a remote destination independent of the originally intended destination and the associated metadata.

BACKGROUND OF THE DISCLOSURE 1. Technical Field

This disclosure relates to automated agents, and specifically toautomated agents that execute document classifications and determinedocument distributions at the page level.

2. Related Art

The conversion and distribution of physical documents is challenging.Documents come in many forms and contain diverse content. The documentsinclude proof of prior insurance, insurance cancellation documents,credit authorization forms, discount forms, uninsured motorist forms,insurance application packets, etc., and any combination of information.The endless sizes, page orientations, layouts, and formats make itnearly impossible to process and translate documents into standardizedforms. Many systems cannot make logical deductions, make logicalinferences, or detect incomplete information. The systems do not learnfrom experiences or analyze contexts.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is better understood with reference to the followingdrawings and description. The elements in the figures are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of the disclosure. Moreover, in the figures, likereferenced numerals designate corresponding parts throughout thedifferent views.

FIG. 1 is a process flow of a processing agent.

FIG. 2 is block diagram of a processing agent.

FIG. 3 is a process flow of a second processing agent.

FIG. 4 is a block diagram of a processing agent integrated within anenterprise.

FIG. 5 is an alternate processing agent.

FIG. 6 is a process flow of a third processing agent.

FIG. 7 is a block diagram third processing agent.

DETAILED DESCRIPTION

A processing system and method (referred to as a processing agent)translate and expedite content delivery of physical and virtualdocuments. The processing agent's end-to-end learning system discoverscontexts and uses contextual knowledge to efficiently route documents todestinations. A detection system accurately detects anomalies, and insome applications, protects backend systems from unsolicited messagesasynchronously transmitted to many recipients. An automated jobseparation system parses documents by breaking documents into pages sothat only desired pages are routed to apt destinations. The jobseparation system enables multiple documents to be processedsimultaneously without job separation sheets.

Some processing agents include export application programming interfaces(APIs) that transmit documents in any desired format to a folder, anaddress, or a destination. Some systems transmit commands that enablescripts. Scripts may request information from remote sources, generateand transmit forms to users, notify users of outcomes, and issue audibleor visual alerts to sources or users that signal an error, missinginformation, or represent a warning. Some document processing agentsexecute data compressions that reduce memory and bandwidth use. Thedocument processing agents execute classifications across multiple nodesallowing the processing agents to distribute classification anddistribution jobs across local and/or remote computing resources.

FIG. 1 is a block diagram of a document processing agent that providesdocument conversions and routing through primary nodes 202 and secondarynodes 204 shown in FIG. 2. The primary nodes 202 pre-process, parse,track, and reassemble select pages of the documents received and storedin an assessment queue that ingests input 102. The secondary nodes 204detect and classify content, validate content, and generate and embedrouting data into individual pages or metadata that is processed toroute the content to correct destinations over the most efficienttransmission route available to the processing agent. Routing occurs atthe page level independent of the original intended destination of thedocument. The independent routing occurs through machine learningwithout relying strictly on deterministic rules. The primary andsecondary nodes 202 and 204 are clients, servers, or peers to otherprimary 202 and secondary nodes 204 and utilize a separate processor orgraphical processor unit that communicate through a communication systemsuch as a bus. The secondary nodes 204 operate asynchronously andindependently of each other and the primary nodes 202 and processdocuments or portions of them simultaneously in parallel (i.e., theprocessing of input in tandem at the same time). Each secondary node 204is analogous to a separate processor with access to local memory thatdetects, classifies, and validates content and generates routing datamapped or directly inserted into metadata and/or the individual pagesthrough a parallel architecture.

In FIG. 1, an assessment queue receives and stores one or more documentsfrom a local or remote source such as a memory or a device (representedas ingest 102). The assessment queue is a multi-element data structurethat adheres to a first-in-first-out (FIFO) processing sequence. Inalternate processing agents, document removal from the assessment queueis based on factors other than the order of insertion; for example, whena priority value is assigned to one or more documents, the priorityvalue may change the output sequence of the assessment queue. In yetanother alternate system, a dequeue is used in which documents are addedor removed from either an insertion input/output (I/O) port or a removalI/O port of the dequeue. That is, the dequeue can operate in alast-in-first-out (LIFO) sequence, a FIFO sequence, or a combination ofLIFO and FIFO sequences during a conversion and/or routing session. Asession refers to a series of requests and responses to complete thetask or a set of tasks such as the processing, reassembly, and/orrouting of pages or documents between clients and servers. A client is adevice that accesses resources from another device via a networkconnection and a server is the device that responds to the client andcontrols access to the network and the server's resources.

In FIG. 1, one or more primary nodes 202 process the documents stored inthe assessment queue. The primary nodes 202 filter the documents todetect content resolution and page orientation. A parser 104 convertsthe documents from one file format to another file format. Theconversion removes the original metadata appended to the documents. Theconversion also rips or extracts pages from documents rendering smallerdocuments or files, with each of ripped page representing no more thanthe size of a physical page. A physical page is one side of a singlesheet of paper. It maybe in standard sizes that are frequentlyreferenced as letter size, legal size, executive size, A4 size, A5 size,A6 size, B5 size, B6 size, postscript size, or an envelope size. In someapplications, the total number of pages in a document determines theminimum number of documents or files rendered by the parser 104. Pagesare ripped at an identifying page, section delimiters, headers, pagebreaks, section breaks, identifiers, etc.

To ensure ripped pages are distributed across computing resources, theprimary node 202 distributes the computational and storage workloadacross the secondary nodes 204 at 106 and tracks page distribution in adata store 206. Distribution optimizes resource use, maximizesthroughput, minimizes response times, and avoids overload conditions atthe secondary nodes 204. In some document processing agents, multipleprimary nodes 202 distribute the ripped pages, which increases thereliability of the processing agent through redundancy. To improvecharacter recognition, each secondary node 204 sharpens text,straightens images (e.g., deskew), applies one or more descreen filtersthat remove artifacts, and/or removes the backgrounds. These processesimprove textual and character matching at 108.

The secondary nodes 204 analyze text and characters by analyzing opticalcontrasts (e.g., a detection of dark and light). Once detected, thesecondary nodes 204 execute a pattern matching with a stored set ofshapes and characters to translate the analyzed elements into an ASCIIcharacter set. Each data element of the character set includes aconfidence score that represents a probability that the results returnedmatch the content contained in the document. Because different typefacesand text treatments, such as bold and italic formats, for example, cansignificantly affect the way text and characters are detected, someprocessing agents execute sophisticated pattern-matching to learn newcharacters, typefaces, and adapt to different character sizes. Alternatesystems achieve high accuracy by executing intelligent word recognition;and other alternates limit input to predefined text and characterformats. In FIG. 1, when a confidence score is below a predeterminedthreshold, the secondary node 204 sets a flag, which terminates therecognition and causes the primary node 202 or secondary node 204 totransmit the subject page(s) to a remote or manual recognition at 110and 118 or execute a recursive analysis of the processing describedabove (e.g., “n” number of repetitions of the classification analysisdescribed). The flag is a marker of some type used by the primary node202 to indicate the existence or status of a particular classificationcondition.

In FIG. 1, an encoding function converts the recognized text andcharacters into a recognition vector. The scalar variable of the vectormay be assigned by a scalar function and adjusted by a weightingfunction that gives some words and phrases and characters of thedocument more “weight” or influence than other words and phrases.Weights may be assigned based on word and phrase counts in a page, theirpresence in an active grammar, or based on their association withpre-identified document types. An active grammar is a file that containsa list of words and phrases recognized by the machine learningalgorithm.

In FIG. 1, the machine learning algorithm designates pages into classesor categories of documents through rules or decision trees that processthe recognition vector at the secondary nodes 204 at 112. The decisiontrees process the recognition vector based on features that eitherclassify the pages or predict the document types that the recognitionvector belongs to. A document type refers to characteristics oridentifiers that represent the characteristics that may be embedded inmetadata or the document itself that identifies or defines the document.It may include elements and attributes. For example, an insurance quote,an insurance application, and an insurance claim are different insurancedocument types.

In FIG. 1, each branch in the decision tree divides data elements intoseveral groups. Each leaf node is allocated with a single label, such asa class or a predicted value. The data is allocated to the appropriateleaf node, and the prediction is the label of that leaf node. To avoidoverfitting, multiple decision trees are added together. For instance,when an exemplary document processing agent attempts to identify aninsurance claim, the predicted identity at any point is the sum of thepredictions of the individual decision trees trained on claim data.

Before use, each decision tree is trained iteratively one decision treeat a time. For example, when a decision tree attempts to identify aninsurance claim page, the system first trains a decision tree on wordsand phrases that are unique to insurance claims. The decision tree istrained to minimize a loss function—such as a mean squared error (whenmean is used) or mean absolute error (when median is used), forexample,—by recursively splitting the training data in a way thatmaximizes a recognition of insurance claim criterion until a limit ismet. The criterion is chosen so that the loss function is minimized byeach split. In some alternate machine learning algorithms, the processis based on an extreme gradient boost.

A second decision tree is then trained to minimize the loss function ofthe first decision tree when its outputs are added to the first decisiontree. This is achieved by recursively splitting the training dataaccording to a second criterion. The criterion may be calculated basedon gradient statistics. A third decision tree is then sequentiallytrained to minimize the loss function of the second decision tree whenits outputs are added to the first and the second decision trees. Thisis achieved by recursively splitting the training data according to athird criterion. Thereafter, “n” number of subsequent trees aresequentially generated and added to the algorithm such that eachsubsequent tree aims to reduce the errors rendered by the previous tree.Each tree learns from its immediate predecessors and updates thelearning algorithm's residual errors. Hence, the tree that grows next inthe sequence will learn from an updated version of the residuals and ineffect render a stronger learned model by effectively combining thepredictive power of all of the prior decision trees.

The time needed to train a tree-based algorithm is proportional to thenumber of splits that are evaluated. Since small changes in the splitmay not significantly affect performance, some methods group featuresinto bins and split the bins instead of the features based on gradientstatistics. This evolutionary process is like subsampling the number ofsplits that the prediction or classification algorithm evaluates. Sincethe features can be binned before building each tree, the evolutionaryprocess speeds up training and reduces computational complexity.

When the characteristics of the recognized vector is classified by themachine learning algorithm at 112 and optional deterministic rules thatexecute comparisons between recognized and predetermined words and/orphrases, the document type classification is generated and embedded ormapped into the page and/or metadata thereafter associated with thepage. Some processing agents then validate the classifications at theprimary or secondary node 202 or 204 at the page level at 114. Forexample, if a page is designated a proof of insurance document, aprimary or secondary node 202 or 204 may execute a comparison againstvalid insurance policy content, valid insurance policy numbers, validinsurance policy formats, valid insurance policy digital signatures(e.g., a comparison to known patterns), or other criteria. Ifunconfirmed, the pages are routed to a reprocessing 118 which may occurvia remote or manual recognition at 118 or via a recursive analysis ofthe processing described above (e.g., “n” number of repetitions of theclassification analysis described). If confirmed, the primary node 202assembles new documents from the classified pages.

Assembly occurs through merger rules and sub-rules at 116. The sub-rulesallow users or devices to issue commands that merge pages through two ormore criteria. For example, an exemplary merger rule may merge pagesbased on a single criterion. A rule may cause the primary node 202 tomerge pages that share a common classification. However, when one ormore rules and one or more sub-rules are executed with a Booleanoperator (e.g., AND, OR, NOT, etc.) or function a more advanced mergingoccurs. For example, a rule and sub-rule combination may cause a primarynode 202 to combine pages that originate from an originating document(e.g., an unparsed document) or source and share a common classificationwith a dynamically generated or originally received cover sheet. Thecover sheet page may serve as a source identifier. It is the page levelclassifications rather than an originating document designation andmerger strategies that determine which pages are routed to separate andunique destinations and where the pages are routed. In some instances,pages are routed to multiple destinations automatically (without userdirections) simplifying a user's online experience and reducing thebandwidth required to support multiple request/response exchanges.

In FIG. 1, merger strategies may be stored in individual profiles inmemory. The profiles allow users to customize merger strategies that maybe based on any property or metadata associated with the classifiedpages, identified document types, and/or originating document and/or anyother criteria or criterion. The profiles may determine how page mergesoccur (e.g., what criteria determines page selection), where the mergesoccur (e.g., at the primary or secondary nodes 202 and 204), thecomputer file formats they occur in (e.g., what format is used, aportable document format, a WORD format, an image format, etc.), whenthey occur (e.g., timing), how the merged pages are compressed, and/orhow the merged pages are distributed (e.g., the medium, email, filetransfers, etc.).

With the processed documents assembled from the page level independentof the composite or arrangement of the original document, the processingagents route the newly assembled document at 120 to one or moredestinations or trigger an alert or enable script at 122 and 124. Thetrigger may occur in response to the classification. In FIG. 1 themetadata generated by machine learning algorithm determines where theprocessed document is routed and whether it triggers a script orinitiates an alert. A script or an alert may be triggered when thedocument is missing information or when it is filled out incorrectly. Asa result, a script may generate a new form, provide additionalinstructions, transmit a message directly to a user or device (e.g., viaan email or via the transmission of a Uniform Resource Locator or URL)that are automatically transmitted to the user or device in response tothe automated routing or manual or remote review. A script is a computerprogram consisting of a set of instructions executed by an applicationor a utility program.

FIG. 3 illustrates a second processing agent in which the digitizedcontent 302 is received and ingested in the assessment queue in memory306 through an I/O request from a remote and/or local source and/ordevice. I/O requests are commands, such as read and write commands, usedto transfer data among various components or portions of components ofthe processing agents. An API 304 interfaces the digitized contentbetween the processing agent platform and the remote and/or localsources and/or devices.

In FIG. 3 one or more primary nodes 202 process the digitized contentstored in the assessment queue. The primary nodes 202 filter the contentto detect image resolution and page orientation. An extractor 308removes metadata originally appended or embedded to or within thedigitized content. The removal may occur via many processes, including aconversion of the digitized content from one computer file format toanother computer file format, or converts it to the same computer fileformat. The conversion removes metadata originally appended, embeddedto, or mapped to the digitized content. The extractor 308 also extractspages or divides/partitions the content from the digitized content filerendering smaller documents or files, with each page representing nomore than a single digital page of the digitized content. A digital pageis a fixed block of memory, consisting of a plurality of bytes,programmed to be read from memory. It is analogous to one side of asingle sheet of physical paper or a portion of a Web page that can beseen on a standard computer display. In some applications, the totalnumber of extracted pages from the digitized content or a predeterminedlength or file size determines the minimum number of documents or filesrendered by the extractor 308. Pages are extracted at visible or hiddenidentifiers, section delimiters, headers, page breaks, section breaks,etc.

A computationally secure encryption process secures the extracted pagesin a database at 310. The encryption uses cryptosystems that arecomputationally infeasibly to break. An algorithm is computationallysecure (sometimes called strong) if it cannot be broken with availableresources, either current or in the future. A self-enforcing hybridcryptosystem that uses controlled session keys and user-controlled keypolicies and/or a volume encryption is an exemplary encryption processused in some processing agents.

To ensure extracted pages are distributed across computing resources,the primary node 202 distributes the computational and storage workloadacross the secondary nodes 204 at 312 and tracks page distribution inthe data store 206. In some processing agents, multiple primary nodes202 distribute the ripped pages. Each secondary node 204 sharpens text,straightens images, applies one or more descreen filters that removeartifacts, and/or removes the backgrounds that improve textual andcharacter matching contrast at 312.

The secondary nodes 204 analyze text and characters through an opticalrecognition. Once detected, the secondary nodes 204 execute a patternmatching with a stored set of shapes and characters to the translate theanalyzed elements into an ASCII character set. Each data element of thecharacter set includes a confidence score that represents a probabilitythat the results returned matches the content contained in the document.Alternate systems achieve high accuracy by executing intelligent wordrecognition. In FIG. 3, when a confidence score is below a predeterminedthreshold, the secondary node 204 terminates the recognition and causesthe primary node 202 or secondary node 204 to transmit the subjectpage(s) to a remote or manual recognition or a recursive analysis of theprocessing described above (e.g., “n” number of repetitions of theclassification analysis described).

In FIG. 3, an encoding function converts the recognized text andcharacters into a recognition vector at 312. The scalar variable of thevector is assigned by a scalar function and adjusted by a weightingfunction. Weights are assigned based on word and phrase counts on apage, their presence in an active grammar, or based on their associationwith pre-identified document types.

In FIG. 3, one or more machine learning algorithms classifies pages intocategories of documents through rules, decision trees, and/or othermodels that process the recognition vector at the secondary nodes 204.The machine learning algorithms process the recognition vector based onfeatures to either classify the pages or predict or classify theextracted pages into the document type or types the recognition vectorbelong to. A document type refers to characteristics or identifiers thatrepresent the characteristics that are embedded in metadata or adocument that identifies or defines the documents.

In FIG. 3, one or more machine learning algorithms may be derived froman initial model that is designed to predict a page and/or documentclassification. The model M₀ is associated with a residual that may berepresented as b−M₀. A second model M₁ is generated to fit or minimizethe residuals of the prior module M₀. The first and the second modelsare combined M₀+M₁ rendering a boosted version of the initial model M₀and result in a lower mean squared error than the initial model M₀. Theequation may be expressed as: M₁(x)<M₀(x)+M₁(x). A third model M₂ maythen be created that models the second networks residuals to improve theperformance of the second model. This is repeated sequentially for “n”iterations until the residuals are minimized to a desired predictionlevel or mean squared error. A generalized expression of the meansquared error maybe expressed as: M_(n)(x)<M_(n−1)(x)+M_(n)(x). As suchthe additive learning algorithms, which may be encompassed in decisiontress, neural networks, etc. do not disturb the functions expressed inthe previous models. Instead, summation of models imparts additionalpredications that reduce errors.

When the characteristics of the recognized vector are classified by themachine learning algorithm at 112 or deterministic rules that matchrecognized words and phrases to predetermined words and phrases and/orcontexts that uniquely identify document types, the document typeclassification is embedded into or mapped to the classified page orassociated to it through metadata. Some processing agents then validatethe classifications at the primary or secondary node 202 or 204 at thepage level. Each validation includes a confidence score that representsa probability that the classification matches the predicted documenttype. For example, if a page is classified as a proof of insurancedocument, a primary or secondary node 202 or 204 may execute acomparison against pre-validated insurance policy content, validinsurance policy numbers, validated insurance policy formats, validateddigital signatures (e.g., a comparison to known patterns), or othercriteria via a field search. If unconfirmed, the pages are routed to areassessment queue 320 that holds the pages until they are reprocessedby the processes described herein or held until an I/O request isreceived at API 322. A request may initiate a different recognitionprocess or a manual process. If or when confirmed, the classified pagesare automatically indexed at 324 and staged in the reassembly queue 326to be used to generate new documents. In FIG. 3, the reassessment queue320 and/or the reassembly queue 326 operate as a dequeue in some systemsand/or adhere to a LIFO, FIFO sequence, or follow a combination of LIFOand FIFO processes in other systems. The API 320 interfaces theprocessing agent platform to remote and/or local sources and/or devices.

Document assembly occurs through merger rules and sub-rules throughprocessor 326. One or more sub-rules allow users or devices to issuecommands that merge pages through different and multiple criteria. Forexample, an exemplary merger rule may merge pages based on a singlecriterion. A rule may cause the primary node 202 to merge pages thatshare a common classification. However, when multiple rules and one ormore sub-rules are executed with a Boolean operator a more advancedmerging occurs. For example, a rule and sub-rule combination may cause aprimary node 202 to combine pages that originate from a common or anoriginating document (e.g., an unparsed document) or source and share acommon classification with a dynamically generated or originallyreceived cover sheet. The cover sheet may be a source identifier.

In FIG. 3, merger strategies may be stored in individual profiles inmemory. The profiles allow users to customize merger strategies that maybe based on any property or metadata associated with the classifiedpages, identified document types, and/or originating document and/oranother criteria or criterion. The profiles may determine when mergingoccur, where they occur, how they occur, the computer file formats theyoccur in, and how the merged pages are compressed and/or how the mergedpages are distributed (e.g., the medium). The compression reduces thememory and bandwidth consumed in storing and transmitting documents.Here, instead of routing entire documents to destinations, processingagents independently and automatically partition the document ordigitized content, and route only the select classified pages (e.g. toone or multiple destinations) based on the machine learning page levelclassifications. In instances where page level classifications haveconflicting routing destinations, the export API 340 routes the selectclassified pages to the highest ranked destinations. This is analogousto a class vote.

With the processed documents assembled at the page level independent ofthe original document, the merged documents are assigned to a job index330. The job index improves the page retrieval operation by maintaininga data structure that can locate every classified page without executinga page search. The job index 330 provides a basis for both rapid randomlookups and efficient access to classified pages and their metadata. Theassembled pages are then routed by an export API 340 that transmits thedocuments to a destination based on the classifications. Routinginformation is retained in a log file as a log entry. A log entry is aset of data entries read from and written to by the router tracker 350that identify the routing information. In FIG. 3 the metadata generatedby machine learning algorithm determines where the processed document isgoing and whether to trigger a script or initiate an alert. A script oralert may occur when a merged document is missing information or wasfilled out incorrectly. As a result, the script may generate a newdocument or form, provide additional instructions to respond to thealert, or transmit a message directly to a user. The message maybedelivered through email or a uniform resource locator.

FIG. 4 is an alternate block diagram that integrates a processing agentinto an enterprise system. In FIG. 4 digitized content 302 is receivedand ingested in the assessment queue in memory through an I/O requestfrom a remote and/or local source and/or device. An integrated APIwithin the primary nodes 202 interfaces the digitized content betweenthe processing agent platform and the remote and/or local sources and/ordevices.

In FIG. 4 a plurality of primary nodes 202 process the digitized contentstored in the assessment queue. The primary nodes 202 filter the contentto detect image resolution and page orientation. It converts the contentfrom one file format to another or same format, which removes metadataoriginally appended, mapped, or embedded to or within the digitizedcontent. The primary nodes 202 also extract pages or divides the contentfrom the digitized content queue rendering smaller documents or files,with each page representing no more than a single digital page of thedigitized content. In some applications, the total number of extractedpages from the digitized content or a predetermined length or file sizedetermines the minimum number of documents or files rendered by theprimary node 202. Pages are extracted at visible or hidden identifiers,section delimiters, headers, page breaks, section breaks, etc.

To ensure extracted pages are distributed across computing resources,the primary nodes 202 distributes the computational and storage workloadacross the secondary nodes 204 (shown as servers 1 to N) and track pagedistribution in the data store 206. Each secondary node 204 sharpenstext, straightens images, applies one or more descreen filters thatremove artifacts, and/or removes the backgrounds that improve textualand character matching contrast at 312.

The secondary nodes 204 analyze text and characters through a characterrecognition. Once detected, the secondary nodes 204 execute a patternmatching with a stored set of shapes and characters to the translate theanalyzed elements into an ASCII character set. Each data element of thecharacter set includes a confidence score that represents a probabilitythat the results returned matches the content contained in the document.Alternate systems achieve high accuracy by executing intelligent wordrecognition. In FIG. 4, when a confidence score is below a predeterminedthreshold, the secondary node 204 terminates the recognition and causesone of the primary nodes 202 or secondary nodes 204 to transmit thesubject page(s) to a remote or manual recognition or initiates arecursive analysis of the processing described above (e.g., “n” numberof repetitions of the classification analysis described).

In FIG. 4, an encoding function executed on the secondary nodes 204converts the recognized text and characters into a recognition vector.The scalar variables of the vector are assigned by a scalar function andadjusted by a weighting function. Weights are assigned based on word andphrase counts on a page, their presence in an active grammar, or basedon their association with pre-identified document types.

In FIG. 4, one or more machine learning algorithms 602 classify pagesinto categories of documents through rules, decision trees, and/or othermodels that process the recognition vector at the secondary nodes 204.The machine learning algorithms described herein process the recognitionvector based on features to either classify the pages or predictextracted pages into the document type or types the recognition vectorbelong to.

When the characteristics of the recognized vector is classified by themachine learning algorithms 402 or deterministic rules that matchrecognized words and phrases to predetermined words and phrases orcontexts that uniquely identify document types, the document typeclassification is embedded into the classified page or associated to itthrough metadata encrypted in memory 310 and 404. Some processing agentsthen validate the classifications at the primary or secondary nodes 202at the page level. Each validation includes a confidence score thatrepresents a probability that the classification matches the predicteddocument type. If unconfirmed, the pages are routed to a reassessmentqueue (not shown) that holds the pages until they are reprocessed by theprocesses described herein or held until an I/O request is received atthe primary nodes 202. A request may initiate a different automatedrecognition process or a manual recognition process. If or whenconfirmed, the classified pages are automatically indexed at thesecondary nodes and staged in a reassembly queue to be processed intonew documents. In FIG. 4, the reassembly queue is integrated within theprimary nodes 202.

In FIG. 4, a control engine within the primary nodes 202 enforces atime-out function for each page spread across the secondary nodes. If aclassification or prediction process exceeds a predetermined time period(e.g., often in minutes or seconds), the primary nodes 202 terminateprocessing at 406, stores the fulfillment items in the data store 206,and clears memory 404. A fulfillment refers to the completeclassification process from receipt of the digitized content in theassessment queue to the mapping or embedding of the classification orprediction in the metadata or extracted pages at the secondary nodes204. Fulfillment also includes the information retained in a log file asa log entry that track all of the processing of the cluster. A clusterrefers to the group of independent network servers and associatedcontrollers that operate and appear to clients (here, the primary nodes202)—as if they were a single unit. In FIG. 4 the five clusters 408-416shown are designed to improve network capacity by, among other things,enabling the servers to process partitioned loads, which enhancesnetwork stability and minimizes data loss when systems fail.

In FIG. 4, the control engines of the primary nodes 202 continuouslymonitor the secondary nodes 204, looking for page classifications asthey are generated. The control engines also track each of theclassification processes, so that if a cluster executes a predeterminedamount of processing time or resources, such as it processed about 75%of the pages it received for example in a predetermined amount of time,the sweep engine pushes the remaining 25% of the pages into areassessment queue that processes the reaming pages as if they wereoriginally received in the assessment queue. In FIG. 4, the sweep engine418 runs at predetermined intervals, such as every ten minutes, forexample, and also terminates at a fixed time period. Here, the controlengine terminates the continuing assessments after about two hours.

Document assembly occurs through merger rules and sub-rules. One or moresub-rules allow users or devices to issue commands that merge pagesthrough different and multiple criteria that is accessible to thefulfillment engines 420 and 422 through the export I/O API 340. In FIG.4, merger strategies may be stored as individual profiles in the datastore 206. The profiles allow users to customize merger strategies thatmay be based on any property or metadata associated with the classifiedpages, identified document types, and/or originating document and/orother criteria or criterion. The profiles may determine when mergersoccur, where they occur, how they occur, the computer file formats theyoccur in and how the merged pages are compressed and/or how the mergedpages are distributed (e.g., the medium).

With the processed documents assembled, the assembled pages are routedby an export API 340 that transmits the documents to a destination basedon the classification. Routing information is retained in a log file asa log entry. In FIG. 4 the metadata generated by machine learningalgorithm determines where the processed document is going and whetherto trigger a script or initiate an alert.

FIG. 5 is an alternate block diagram of the processing agent of FIG. 2.In FIG. 5, the communication bus of FIG. 2 is replaced by a network thatallows the primary nodes 202, the secondary nodes 204, and the datastore 206, to be hosted on remote distributed systems. The primary nodes202, the secondary nodes 204, and the data store 206 and theiralternates function as described above and herein.

FIG. 6 is an alternate block diagram of the processing agent process ofFIG. 1. In FIG. 6, documents are received through the assessment queueand computer vision. Computer vision may stand alone, may be integratedwith, or may be a unitary part of the primary nodes 202, the secondarynodes 204, the extractor 308 or any of the other modules, elements, orlogic described herein. Physical objects may be identified through thecomputer vision engine 602 that may render an image document or a videosequence. The computer vision results may include physical objectidentification information, physical object position and orientationinformation, numerical measurement data, counts, and pre-designationsclassifications of physical objects, images of the physical object, andconfidence values related to the physical object identification that arerendered as image documents. The remaining functions shown in FIG. 6 andtheir alternates function as described herein.

FIG. 7 is a block diagram of a second alternate block diagram of thealternate processing agent of FIG. 2. The system comprises multipleprocessors 718-730 (e.g., CPUs, GPUs, etc.), multiple non-transitorymedia 702-716 such as multiple memories (the contents of which areaccessible to the processors 718-730, respectively). The memories702-716 may store instructions which when executed by one or more of theprocessors 718-730, respectively, causes the systems and methods torender some or all of the functionality associated with the processingagents and some or all of the functionality of the evolutionaryprocesses that generate the machine learning algorithms. For example,the memory 702-716 may store instructions which when executed by one ormore of the processor 718-730, respectively, causes the system to renderthe functionality associated with one or more secondary nodes 204 (thesymbol

shown in the secondary node blocks 204 establishes the other elementsthat comprise the secondary nodes 204 are hidden behind the blockrepresentation if not explicitly shown), the assessment queue 732,reassessment queue 734, the data store 206, the primary nodes 202, thereassembly logic 116, the validation logic 114, the routing logic 120,the encryption cipher 310, the learning algorithm 402, the primary nodes202 (the symbol

shown in the primary node blocks 202 indicates the other elements thatcomprise the primary nodes 202 are hidden behind the blockrepresentation if not explicitly shown), the mapping logic 106, the autoindex logic 320, the router tracker logic 350, the I/O APIs 122, 304 and322, the export logic 340, the archive 360, the alert logic and scripts124, the SQL databases 206, the seep logic 418, and/or the controlengine. In addition, data structures, temporary variables, metadata andother information are stored in one or more memories 702-716.

The processors 718-730 may comprise a single processor with multiplecores or multiple processors with multiple cores, on multiple devices ordistributed across more than one system that run in parallel. Theprocessors 718-730 may be hardware that executes computer executableinstructions or computer code embodied in the memory 702-716 or in othermemory to perform one or more features of the disclosed system. Theprocessors 718-730 may include a central processing unit (CPU), agraphics processing unit (GPU), an application specific integratedcircuit (ASIC), a digital signal processor (DSP), a field programmablegate array (FPGA), a digital circuit, an analog circuit, amicrocontroller, any other type of processor, or any combinationthereof.

The memories 702-716 or storage disclosed may retain an ordered listingof executable instructions for implementing the functions describedherein. The machine-readable medium may selectively be, but not limitedto, an electronic, a magnetic, an optical, an electromagnetic, aninfrared, or a semiconductor medium. A non-exhaustive list of examplesof a machine-readable medium includes: a portable magnetic or opticaldisk, a volatile memory, such as a Random-Access Memory (RAM), aRead-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROMor Flash memory), or a database management system. The memories 702-716may comprise a single device or multiple devices that may be disposed onone or more dedicated memory devices or on a processor or other similardevice.

The memories 702-716 may also store computer code that may includeinstructions executable by the processor 718-730. The computer code maybe written in any computer language, such as C, C++, assembly language,channel program code, and/or any combination of computer languages. Thememories 702-716 may store information in data structures.

The functions, acts or tasks illustrated in the figures or described maybe executed in response to one or more sets of logic or instructionsstored in or on non-transitory computer readable media as well. Thefunctions, acts or tasks are independent of the particular type ofinstructions set, storage media, processor or processing strategy andmay be performed by software, hardware, integrated circuits, firmware,micro code and the like, operating alone or in combination. In oneembodiment, the instructions are stored on a removable media deviceaccessible to a remote machine. In other embodiments, the logic orinstructions are stored in a remote location for transfer through acomputer networks or over wireless or tangible communication lines. Inyet other embodiments, the logic or instructions may be stored andexecuted by multiple GPU servers.

While each of the processing agents shown and described herein operateautomatically and operate independently, they also may be encompassedwithin other systems and methods and execute any number “n” ofiterations of some or all of the process used to enhance documents,recognize pages, render classification and/or route to destinations.Alternate processing agent may include any combinations of structure andfunctions described or shown in one or more of the FIGS. These automatedprocessing systems are formed from any combination of structures andfunctions described herein. The structures and functions may processadditional or different input. For example, alternate machine learningalgorithms may perform classification based on a contextual analysistoo. By including contexts in the training of the machine learningalgorithm, the machine learning algorithm's automatic classificationsare not limited to the processing of recognition vector to decisiontrees trained only on word and phrase combinations. This context-basedtraining constitutes an improvement over traditional training as themachine learning algorithms are also based on user's intentionsreflected in contexts expressed in the documents (via recognizingholistic context-sensitive exchanges by considering statements thatsurround a word or passage), rather than being based exclusively onisolated word and phrase input. The inclusion of sub-textual analysis inthe machine algorithm's training enables a more flexible, nuanced, andaccurate classification that can easily be tailored to the processingagent's applications.

Consider content that includes the term “comprehensive” in multiplepages. In these exemplary pages, the machine learning algorithmconcludes that the user has demonstrated an unfamiliarity with insurancebased on the user's failure to provide responses to common insurancequestions rendered on the form. In viewing the context, thepredetermined number of unanswered questions, the information providedon the document indicate that document is not from a customer of theinsurance carrier (e.g., confirmed via a credential verification), andthe use of the term “comprehensive” in the document, the machinelearning algorithm associates and clarifies these contexts as pagesrequesting a bundled insurance quote.

If a machine learning algorithm were trained on the term “comprehensive”alone without context, and specifically with respect to automobiles, theterm “comprehensive” would be understood to refers to one of threeinsurance coverages. The two other coverages are liability andcollision. Collision covers damage to vehicles following a collision,and comprehensive fills in the gaps by covering damage to vehiclescaused by anything other than a collision. While a recognition of theinput alone would mistakenly classify the document as a request forinsurance policy serving that should be routed to an insurance policyservicing destination; by including contexts and contextual associationsin the machine learning algorithm training, the machine learningalgorithm automatically classifies the pages as a document related to aninsurance quote.

In yet another alternative processing agent, a detection systemaccurately detects anomalies in classified documents, and in responseissues alerts or requests for supplemental information by transmittingrequest for clarifications via email or serving a URL. In someapplications, when a predetermined number of unsolicited messages areasynchronously transmitted to many recipients, the machine learningalgorithm also can classify the pages as spam and cause the computersystem via an alert or script to block the documents due to the largenumber of similar messages received. The term spam refers to sending thesame message indiscriminately to (large numbers of recipients).

The term “machine learning” refers to devices or machines that executemachine learning algorithms which use processing units' for characterrecognition. Some machine learning devices represent concepts inmultiple hierarchical fashion with corresponds to various levels ofabstraction. The term “coupled,” disclosed in this description mayencompass both direct and indirect coupling. Thus, a first and a secondelement are said to be coupled when they communicate directly with oneanother, as well as when the first element communicates through anintermediate component, which is connected directly or via one or moreadditional intermediate components to a second element. The term“substantially” or “about” may encompass a range that is largely, butnot necessarily wholly, what is specified. It encompasses all but aninsignificant amount, such as a variance within a range of five or tenpercent of the given value. When devices are responsive to commandsevents, and/or requests, the actions and/or steps of the devices, suchas the operations that devices are performing, necessarily occur as adirect or an indirect result of the preceding commands, events, actions,and/or requests. In other words, the operations occur as a result of thepreceding operations. A device that is responsive to another requiresmore than an action (i.e., the device's response to) merely followanother action.

A processing agent translate and expedite content delivery of physicaland virtual documents. The physical and virtual documents includedocuments or pages that contain unstructured data that either does nothave a predefined data model or is not organized in a pre-definedmanner. An end-to-end learning system learns contexts and uses itscontextual knowledge to efficiently route documents to destinations. Adetection system accurately detects anomalies, and in some applications,protects backend systems from unsolicited messages asynchronouslytransmitted to many recipients. An automated job separation systemparses documents by breaking documents into pages so that only desiredpages are routed to apt destinations. The job separation system enablesmultiple documents to be processed simultaneously without job separationsheets.

Some processing agents include an export API that transmits documents inany desired format to a folder, an address, or a destination. Somesystems transmit commands that enable scripts. Scripts may requestinformation from remote sources, generate and transmit forms to users,notify users of outcomes, and issue audible or visual alerts to sourcesor users that signal an error, missing information, or represent awarning. Some document processing agents execute data compressions thatreduces memory and bandwidth use. The document processing agents executeacross multiple nodes allowing the processing agents to distribute jobsacross local and/or remote computing resources.

The subject-matter of the disclosure may also relate, among others, tothe following aspects (referenced by numbers):

1. A method of classifying insurance documents having insurance data,the method comprising:

receiving a request to access a plurality of documents in an assessmentqueue stored in a memory of a device; each of the plurality of documentsare made up of pages containing insurance data and have an associatedpre-defined destination and an associated metadata;

converting a set of documents encoded in a first file format for storagein a non-transitory computer media into a second file format thatremoves the metadata associated with each document of the set ofdocuments;

partitioning each document of the set of documents into separatestand-alone documents such that each partitioned document represents nomore than a physical page;

converting each of the partitioned documents into separate recognitionvectors that represent information conveyed in each of the partitioneddocuments;

classifying the partitioned documents through an additive learningalgorithm in which routing data is embedded in second metadataassociated with each of the partitioned documents;

merging the classified partitioned documents in response to a pluralityof rules based at least in part on the second metadata; and

causing the merged documents to be routed to a remote destinationindependent of the predefined destination and the metadata.

2. The method of aspect 1 where the plurality of documents comprisesemail and digital content.

3. The method of any of aspects of 1 to 2 where the first file format isdifferent from the second file format.

4. The method of any of aspects of 1 to 3 further comprisingdistributing the partitioned documents across a plurality of servers.

5. The method of any aspects of 1 to 4 further comprising executing anoptical contrast to detect the letters and characters contained in theset of documents.

6. The method of any aspects of 1 to 5 further comprising applying aweighting to scalar variables that comprise the recognition vectorsbased on an active grammar or a predefined document type.

7. The method of any aspects of 1 to 6 where the additive learningalgorithm comprises a decision tree.

8. The method of aspect 7 where the decision tree is a boosted decisiontree.

9. The method of aspect 7 where the time required to train the additivelearning algorithm is proportional to a number of splits executed on thedecision tree.

10. The method of aspect 7 where the decision tree is trained oncontextual associations between words and phrases.

11. The method of any aspects of 1 to 10 further comprising causing thepartitioned documents to undergo a data compression that reduces theamount of memory required to store the partitioned documents.

12. The method of any aspects of 1 to 11 where the merging of thepartitioned documents is based on at least on one Boolean function.

13. The method of any aspects of 1 to 12 where the plurality of rules isstored in individual profiles in a memory which determine when themerging occurs, where the merging occurs, or how the merging occurs.

14. The method of any aspects of 1 to 13 where the plurality of rules isstored in individual profiles in a memory which determine computer fileformats that the merging occurs or how the merged partitioned documentsare compressed or mediums that distribute the merged documents.15. The method of any aspects of 1 to 14 further comprising causing theissuance of a warning in response to the classification of thepartitioned documents.16. The method of any aspects of 1 to 15 further comprising initiating ascript in response to the classification of the partitioned documents.17. A non-transitory machine-readable medium encoded withmachine-executable instructions for classifying insurance documentshaving insurance data, where execution of the machine-executableinstructions is for:

receiving a request to access a plurality of documents in an assessmentqueue stored in a memory of a device; each of the plurality of documentsare made up of pages containing insurance data and have an associatedpre-defined destination and an associated metadata;

converting a set of documents encoded in a first file format for storagein a non-transitory computer media into a second file format thatremoves the metadata associated with each document of the set ofdocuments;

partitioning each document of the set of documents into separatestand-alone documents such that each partitioned document represents nomore than a physical page;

converting each of the partitioned documents into separate recognitionvectors that represent information conveyed in each of the partitioneddocuments;

classifying the partitioned documents through an additive learningalgorithm in which routing data is embedded in second metadataassociated with each of the partitioned documents;

merging the classified partitioned documents in response to a pluralityof rules based at least in part on the second metadata; and

causing the merged documents to be routed to a remote destinationindependent of the predefined destination and the metadata.

18. The non-transitory machine-readable medium of any aspect 17 wherethe plurality of documents comprises email and digital content.

19. The non-transitory machine-readable medium of any aspects of 17 to18 where the first file format is different from the second file format.

20. The non-transitory machine-readable medium of any aspects of 17 to19 further comprising distributing the partitioned documents across aplurality of servers.

21. The non-transitory machine-readable medium of any aspects of 17 to20 further comprising executing an optical contrast to detect lettersand characters contained in the set of documents.

22. The non-transitory machine-readable medium of any aspects of 17 to22 further comprising applying a weighting to scalar variables thatcomprise the recognition vectors based on an active grammar or apredefined document type.

23. The non-transitory machine-readable medium of any aspects of 17 to22 where the additive learning algorithm comprises a decision tree.

24. The non-transitory machine-readable medium of any aspect of 23 wherethe decision tree is a boosted decision tree.

25. The non-transitory machine-readable medium of any aspect of 23 wherethe time required to train the additive learning algorithm isproportional to a number of splits executed on the decision tree.

26. The non-transitory machine-readable medium of any aspect of 23 wherethe decision tree is trained on contextual associations between wordsand/or phrases.

27. The non-transitory machine-readable medium of any aspects of 17 to26 further comprising causing the partitioned documents to undergo adata compression that reduces the amount of memory required to store thepartitioned documents.

28. The non-transitory machine-readable medium of any aspects of 17 to27 where the merging of the partitioned documents is based on at leastone Boolean function.

29. The non-transitory machine-readable medium of any aspects of 17 to28 where the plurality of rules is stored in individual profiles in amemory which determine when the merging occurs, where the mergingoccurs, or how the merging occurs.

30. The non-transitory machine-readable medium of any aspects of 17 to29 where the plurality of rules is stored in individual profiles in amemory which determine computer file formats that the merging occurs,how the merged partitioned documents are compressed or mediums that themerged documents are distributed.31. The non-transitory machine-readable medium of any aspects of 17 to31 further comprising causing the issuance of a warning in response tothe classification of the partitioned documents or initiating a scriptin response to the classification of the partitioned documents.32. A system that classifies insurance documents having insurance data,the system comprising:

an assessment queue storing a plurality of documents in a memory of adevice; each of the plurality of documents comprise pages containinginsurance data, each document having an associated pre-defineddestination and associated metadata;

a plurality of primary nodes representing a plurality of independentprocessing units that automatically:

-   -   convert a set of documents encoded in a first file format for        storage in a non-transitory computer media to a second file        format that removes the metadata associated with each document;        and    -   partition each document of the set of documents into separate        stand-alone documents such that each partitioned document        represents no more than a physical page;

a plurality of secondary nodes representing a plurality of independentprocessing units that automatically:

-   -   convert each of the partitioned documents into separate        recognition vectors that represent the information contained in        the partitioned documents;    -   classify the partitioned documents through an additive learning        algorithm in which routing data is embedded in second metadata        associated with each of the partitioned documents;    -   merge the classified partitioned documents based on a plurality        of rules based at least in part on the second metadata; and    -   cause the merged documents to be routed to a remote destination        independent of the predefined destination.        33. The system of any aspects of 32 where the plurality of        documents comprises email and digital content.        34. The system of any aspects of 32 to 33 where the first file        format is different from the second file format.        35. The system of any aspects of 32 to 35 further comprising        distributing the partitioned documents across a plurality of        servers.        36. The system of any aspects of 32 to 35 further comprising        executing an optical contrast to detect letters and characters        contained in the set of documents.        37. The system of any aspects of 32 to 35 further comprising        applying a weighting to scalar variables that comprise the        recognition vectors based on an active grammar or a predefined        document type.        38. The system of any aspects of 32 to 35 where the additive        learning algorithm comprises a decision tree.        39. The system of any aspect of 38 where the decision tree is a        boosted decision tree.        40. The system of any aspect of 38 where the time required to        train the additive learning algorithm is proportional to a        number of splits executed on the decision tree.        41. The system of any aspect of 38 where the decision tree is        trained on contextual association between words and/or phrases.        42. The system of any aspects of 32 to 41 further comprising        causing the partitioned documents to undergo a data compression.        43. The system of any aspects of 32 to 42 where the merging of        the partitioned documents is based on at least one Boolean        function.        44. The system of any aspects of 32 to 43 where the plurality of        rules is stored in individual profiles in a memory which        determine when the merging occurs, where the merging occurs, or        how the merging occurs.        45. The system of any aspects of 32 to 44 where the plurality of        rules is stored in individual profiles in a memory which        determine computer file formats that the merging occurs or how        the merged partitioned documents are compressed or mediums that        the merged documents are distributed.        46. The system of any aspects of 32 to 45 further comprising        causing the issuance of a warning in response to the        classification of the partitioned documents.        47. The system of any aspects of 32 to 46 further comprising        initiating a script in response to the classification of the        partitioned documents.

Other systems, methods, features and advantages will be, or will become,apparent to one with skill in the art upon examination of the figuresand detailed description. It is intended that all such additionalsystems, methods, features and advantages be included within thisdescription, be within the scope of the disclosure, and be protected bythe following claims.

What is claimed is:
 1. A method of classifying insurance documentshaving insurance data, the method comprising: receiving a request toaccess a plurality of documents in an assessment queue stored in amemory of a device; each of the plurality of documents is made up ofpages containing insurance data and have an associated predefineddestination and an associated metadata before the plurality of documentsare read; converting a set of documents encoded in a first file formatfor storage in a non-transitory computer media into a second file formatthat removes all of the metadata associated with each document of theset of documents; partitioning each document of the set of documentsinto separate stand-alone documents such that each partitioned documentrepresents no more than a physical page; converting each of thepartitioned documents into separate recognition vectors that representinformation conveyed in each of the partitioned documents; classifyingthe partitioned documents through machine learning algorithms comprisinga plurality of learning models that are combined to generate a summedoutput; the plurality of learning models includes a successive learningmodel that minimizes a plurality of residuals generated from a precedinglearning model; processing the summed output as an input to a subsequentlearning model separate from the plurality of learning models thatembeds routing data in second metadata within and associated with eachof the partitioned documents that reduces a prediction error; mergingthe classified partitioned documents in response to a plurality of rulesbased at least in part on the second metadata; and causing the mergeddocuments to be routed to a remote destination independent of thepredefined destination and the associated metadata.
 2. The method ofclaim 1 where the plurality of documents comprises email and digitalcontent.
 3. The method of claim 1 where the first file format isdifferent from the second file format.
 4. The method of claim 1 furthercomprising distributing the partitioned documents across a plurality ofservers.
 5. The method of claim 1 further comprising executing anoptical contrast to detect letters and characters contained in the setof documents.
 6. The method of claim 1 further comprising applying aweighting to scalar variables that comprise the recognition vectorsbased on an active grammar or a predefined document type.
 7. The methodof claim 1 where the machine learning algorithms include a decisiontree.
 8. The method of claim 7 where the decision tree comprises aboosted decision tree.
 9. The method of claim 7 where a time required totrain the machine learning algorithms is proportional to a number ofsplits executed on the decision tree.
 10. The method of claim 7 wherethe decision tree is trained on a plurality of words or phrases and acontextual association between the plurality of the words or phrasesthat comprise one or more statements that surround the plurality ofwords or phrases that represent a user's intentions.
 11. The method ofclaim 1 where the merging of the partitioned documents is based on atleast one Boolean function.
 12. The method of claim 1 where theplurality of rules is stored in individual profiles in a memory which,determine when the merging occurs, where the merging occurs, or how themerging occurs.
 13. The method of claim 1 where the plurality of rulesis stored in individual profiles in a memory which determine computerfile formats that the merging occur or how the merged documents arecompressed or mediums that distribute the merged documents.
 14. Themethod of claim 1 further comprising causing issuance of a warning inresponse to the classification of the partitioned documents.
 15. Themethod of claim 1 further comprising initiating a script in response tothe classification of the partitioned documents.
 16. A non-transitorymachine-readable medium encoded with machine-executable instructions forclassifying insurance documents having insurance data, where executionof the machine-executable instructions is for: receiving a request toaccess a plurality of documents in an assessment queue stored in amemory of a device; each of the plurality of documents are made up ofpages containing insurance data and have an associated destination andan associated metadata before the plurality of documents are read;converting a set of documents encoded in a first file format for storagein a non-transitory computer media into a second file format thatremoves all of the metadata associated with each document of the set ofdocuments; partitioning each document of the set of documents intoseparate stand-alone documents such that each partitioned documentrepresents no more than a physical page; converting each of thepartitioned documents into separate recognition vectors that representinformation contained in each of the partitioned documents; classifyingthe partitioned documents through machine learning algorithms comprisinga plurality of learning models that are combined to generate a summedoutput; the plurality of learning models includes a successive learningmodel that minimizes a plurality of residuals generated from a precedinglearning model; processing the summed output as an input to a subsequentlearning model separate from the plurality of learning models in whichrouting data is embedded in second metadata associated with each of thepartitioned documents that reduces a prediction error; merging theclassified partitioned documents in response to a plurality of rulesbased at least in part on the second metadata; and causing the mergeddocuments to be routed to a remote destination independent of thepredefined destination and the associated metadata.
 17. Thenon-transitory machine-readable medium of claim 16 where the pluralityof documents comprises email and digital content.
 18. The non-transitorymachine-readable medium of claim 16 where the first file format isdifferent from the second file format.
 19. The non-transitorymachine-readable medium of claim 16 further comprising distributing thepartitioned documents across a plurality of servers.
 20. Thenon-transitory machine-readable medium of claim 16 further comprisingapplying a weighting to scalar variables that comprise the recognitionvectors based on an active grammar or a predefined document type. 21.The non-transitory machine-readable medium of claim 16 where the machinelearning algorithms include a decision tree.
 22. The non-transitorymachine-readable medium of claim 21 where the decision tree is a boosteddecision tree.
 23. The non-transitory machine-readable medium of claim21 where a time required to train the machine learning algorithms isproportional to a number of splits executed on the decision tree. 24.The non-transitory machine-readable medium of claim 21 where thedecision tree is trained on a plurality of words or phrases and aplurality of contextual associations between the plurality of words orphrases that comprise one or more statements that surround the pluralityof words or phrases that represent a user's intentions.
 25. Thenon-transitory machine-readable medium of claim 16 further comprisingcausing the partitioned documents to undergo a data compression.
 26. Thenon-transitory machine-readable medium of claim 16 where the merging ofthe partitioned documents is based on at least one Boolean operator. 27.The non-transitory machine-readable medium of claim 16 where theplurality of rules is stored in individual profiles in a memory, whichdetermine when the merging occurs, or where the merging occurs or howthe merging occurs.
 28. The non-transitory machine-readable medium ofclaim 16 where the plurality of rules is stored in individual profilesin a memory which determine computer file formats that the mergingoccurs or how the merged partitioned documents are compressed or mediumsthat distribute the merged documents.
 29. A non-transitorymachine-readable medium encoded with machine-executable instructions forclassifying documents, where execution of the machine-executableinstructions is for: receiving a request to access a plurality ofdocuments in an assessment queue stored in a memory of a device; each ofthe plurality of documents are made up of pages containing content dataand have an associated predefined destination and an associated metadatabefore the plurality of documents are read; removing all of the metadataassociated with each document of a set of documents; partitioning eachdocument of the set of documents into separate stand-alone documentssuch that each partitioned document represents no more than a physicalpage; converting each of the partitioned documents into separaterecognition vectors that represent information contained in each of thepartitioned documents; classifying the partitioned documents through amachine learning algorithm comprising a plurality of learning modelsthat are combined to generate a summed output; the plurality of learningmodels includes a successive learning model that minimizes a pluralityof residuals generated from a preceding learning model; processing thesummed output as an input to a subsequent learning model separate fromthe plurality of learning models that embeds routing data in secondmetadata within and associated with each of the partitioned documentsthat reduces a prediction error; merging the classified partitioneddocuments in response to a plurality of rules based at least in part onthe second metadata; and causing the merged documents to be routed to aremote destination independent of the associated predefined destinationand the associated metadata.
 30. The non-transitory machine-readablemedium of claim 29 where the physical page comprises a file of apredetermined length.